Systems and methods for creating and enhancing videos

ABSTRACT

Embodiments of the present disclosure help to automatically generate video selected from multiple video sources using intelligent sensor processing, thereby providing viewers with a unique and rich viewing experience quickly and inexpensively.

CROSS-REFERENCES TO RELATED APPLICATION

This application claims priority to U.S. Provisional Patent Application No. 62/056,868, filed Sep. 29, 2014 and entitled “CROWDSOURCING AUTO CREATION AND ENHANCING OF ACTION VIDEOS,” and U.S. Provisional Patent Application No. 62/025,963, filed Jul. 17, 2014 and entitled “USE OF GYRO SENSORS FOR IDENTIFYING ATHLETIC MANEUVERS,” and U.S. Provisional Patent Application No. 62/001,935, filed May 22, 2014 and entitled “USE OF GYRO SENSORS FOR JUMP DETECTION,” the disclosures of which are hereby incorporated herein by reference.

This application is related to U.S. patent application Ser. No. 14/024,502, filed Sep. 11, 2013 and entitled “SYSTEMS AND METHODS FOR SYNCHRONIZED DISPLAY OF ATHLETIC MANEUVERS,” and to U.S. patent application Ser. No. 13/734,818, filed Jul. 1, 2013 and entitled “AUTOMATIC DIGITAL CURATION AND TAGGING OF ACTION VIDEOS,” and to U.S. patent application Ser. No. 13/932,899, filed Jul. 1, 2013 and entitled “SYSTEMS AND METHODS FOR IDENTIFYING AND CHARACTERIZING ATHLETIC MANEUVERS,” and to U.S. patent application Ser. No. 13/612,470, filed Sep. 12, 2012 and entitled “METHOD AND APPARATUS FOR DETERMINING SPORTSMAN JUMPS USING FUZZY LOGIC,” the disclosures of which are hereby incorporated herein by reference.

BACKGROUND

The popularity of extreme sports (also referred to as “action sports” or “adventure sports”) is steadily growing. In particular, sports such as snowboarding, skateboarding, free skiing, surfboarding, skydiving, wingsuit flying, bicycle motocross (BMX), and others are becoming (or are currently) mainstream sports. Such sports are increasingly being covered by various media organizations and some competitions (such as the X-Games) are devoted solely to extreme sports.

In many traditional sports, competitions are measured and judged using objective measures such as scores (e.g., the number of runs in a baseball game or points in a basketball game) or times (e.g., the time for a runner or downhill skier to cross a finish line). In many extreme sports, by contrast, athletes compete by performing various athletic maneuvers (or “tricks”) such as jumps, flips, rotations, and the like. As an example, a description of common tricks that are performed in snowboarding can be found at http://en.wikipedia.org/wiki/List_of_snowboard_tricks and a description of common skateboarding tricks at http://skateboardingtrickslist.com.

Many athletic maneuvers in extreme sports are complex and/or performed very quickly. Accordingly, athletic maneuvers performed in action sports competitions are often evaluated (by judges and spectators) based on a variety of subjective factors, including an individual's personal perception of the difficulty or aesthetics of a particular athletic maneuver. This often leads to problems in identifying different athletic maneuvers, as well as judging or rating such maneuvers in competition. In ski jump competitions, for example, the movement of the jumper may be so fast that spectators cannot determine the number of rotations or flips the jumper performs.

Some conventional systems attempt to measure fast rotations with inertial sensors. Such measurements are typically made relative to the sensor axis but can be related to the absolute axis as well. However, such conventional systems do not make sensor measurements understandable to the sport participants and spectators. Conventional systems are unable to automatically recognize and measure action sport “tricks,” and are thus unsuitable for virtual competitions, leaderboards, and social networks dedicated to the action sport participants.

Additionally, fans of traditional and extreme sporting events often record videos of such events using video cameras, their smart phones, or other video-capturing devices. This provides a large number of possible video sources for various events, which can be further supplemented by video drones and other systems.

While such recording is often not professional, it provides a vast amount of video data that could be mined to create a unique viewing experience. However, the very nature of having multiple video sources, many of which may be unattended and/or uncoordinated, creates a serious challenge for data selection and editing. Embodiments of the present disclosure address these and other issues.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of certain embodiments may be derived by referring to the detailed description and claims when considered in connection with the following illustrative figures.

FIGS. 1 and 1A are flow diagrams showing exemplary processes according to various embodiments.

FIG. 2 is a block diagram of an exemplary system according to various embodiments.

FIGS. 3, 4, 5, 6A, 6B, and 7 are exemplary graphs according to various aspects of the present disclosure.

FIGS. 8-12 are additional exemplary graphs according to various aspects of the present disclosure.

FIG. 13 illustrates and example of multiple video sources capturing video of an actor according to various aspects of the present disclosure.

FIG. 14 is a flow diagram showing an exemplary process according to various embodiments.

FIG. 15 is a flow diagram showing another exemplary process according to various embodiments.

SUMMARY

Embodiments of the present disclosure help to automatically generate video selected from multiple video sources using intelligent sensor processing, thereby providing viewers with a unique and rich viewing experience quickly and inexpensively.

A computer-implemented method according to one embodiment of the present disclosure comprises receiving, by a computer system, first video of an actor from a first source, wherein the first video is captured during a first time period; receiving, by the computer system, second video of the actor from a second source, wherein the second video is captured during a second time period, and wherein the first time period and second time period at least partially overlap; receiving, by a computer system, sensor data related to motion by the actor over one or more of the first time period and the second time period; determining, by the computer system and based on the sensor data, a plurality of motion characteristics; identifying, based on the plurality of motion characteristics, an athletic maneuver associated with the motion; creating, by the computer system, a combined video that displays the actor performing the athletic maneuver and includes at least one frame of the first video from the first source and at least one frame of the second video from the second source.

The present disclosure includes various methods, apparatuses (including computer systems) that perform such methods, and computer readable media containing instructions that, when executed by computing systems, cause the computing systems to perform such methods.

Other features will be apparent from the accompanying drawings and from the detailed description which follows.

DETAILED DESCRIPTION

The following description and drawings are illustrative and are not to be construed as limiting. Numerous specific details are described to provide a thorough understanding. However, in certain instances, well known or conventional details are not described in order to avoid obscuring the description. References to one or an embodiment in the present disclosure are not necessarily references to the same embodiment; and, such references mean at least one.

Reference in this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, various features are described which may be exhibited by some embodiments and not by others. Similarly, various requirements are described which may be requirements for some embodiments but not other embodiments.

Any combination and/or subset of the elements of the methods depicted herein may be practiced in any suitable order and in conjunction with any suitable system, device, and/or process. The methods described and depicted herein can be implemented in any suitable manner, such as through software operating on one or more computer systems. The software may comprise computer-readable instructions stored in a tangible computer-readable medium (such as the memory of a computer system) and can be executed by one or more processors to perform the methods of various embodiments.

FIG. 1 depicts an exemplary process according to various embodiments of the present disclosure. In FIG. 1, method 100 includes receiving sensor data (105) related to motion by an actor over a period of time and determining motion characteristics based on the sensor data (110). Method 100 further includes receiving input related to a definition of an athletic maneuver (115), associating one or more motion characteristics with the definition (120), and storing the athletic maneuver definition and associated motion characteristics in a database (125). Method 100 further includes identifying one or more athletic maneuvers based on the determined motion characteristics (130), determining a level of similarity between the determined motion characteristics and motion characteristics associated with the identified athletic maneuver(s) (135), and generating an alert (140) in response to the similarity level being below a predetermined threshold. Method 100 also includes combining or overlaying information related to the athletic maneuver with video of the athletic maneuver (145). The steps of method 100 may be implemented (in whole or in part, and in any desired order) by software operating on a computer system, such as the exemplary computer system 200 depicted in FIG. 2.

Embodiments of the present disclosure may receive sensor data (105) directly or indirectly from any number and type of sensors, such as an accelerometer, a gyroscope, a magnetometer, a Hall effect sensor, a global positioning system, an ultrasonic sensor, an optical sensor; a barometric sensor; and combinations thereof. Information from different sensors may be used together or separately to determine various motion characteristics for the actor and/or the actor's equipment. As used in this context, an “actor” performing an athletic maneuver may refer to any human (e.g., skier, snowboarder, cyclist, diver, athlete, etc.) as well as to sporting equipment associated with, or controlled by, a human. Such sporting equipment may include, for example, a vehicle (such as a bicycle, boat, automobile, motorcycle, etc.), a skateboard, skis, a snowboard, a parachute, and other equipment or devices.

For example, Hall effect sensors may be used to monitor the speed of a stunt bicyclist's wheels before, during, and after a jump, while data from one set of accelerometers and gyroscopes can monitor flips and rotations performed by the bicyclist and a second set of accelerometers and gyroscopes can monitor flips and rotations of the bicycle itself Other sensors, such as an optical sensor (e.g., a proximity sensor) or global positioning system can be used to help determine actor's position with regards to the ground, ramp, other actors, and other objects. Various embodiments may utilize data from sensors imbedded in or attached to an actor's clothing, skin, equipment, or surroundings (e.g., in a ramp used by an actor to perform jumps). The sensor data may be received in any suitable manner, such as wirelessly from a data collection system coupled to an actor and/or an actor's equipment. The time interval when a maneuver or “trick” is performed can be determined by identifying and measuring a jump by monitoring and analyzing characteristic signature of the gyro sensors using fuzzy logic as described in U.S. patent application Ser. No. 13/612,470, the contents of which are incorporated by reference herein. For example, FIG. 7 compares a signal from accelerometers and gyro sensors recorded by a device that was attached to a skateboard during maneuvers performed by a skateboarder. To aid in separating the signals, the accelerometer norm was shifted up by 1000 mili-g. FIG. 7 shows that while accelerometer signal is very noisy due to the board vibration, the gyro signal is much better correlated with the trick time period.

Embodiments of the present disclosure can be particularly effective in identifying and characterizing athletic maneuvers for extreme sports based on objective data. Additionally, various embodiments may be used in conjunction with various other sports and activities, such as baseball, basketball, hockey, soccer, and football. For example, embodiments of the present disclosure may be configured to receive data from sensors coupled to a baseball player's bat, as well as from sensors attached to the player's uniform and/or embedded in a baseball. In one such embodiment, information regarding a player hitting a baseball, such as the angle of the player's swing, the velocity of the bat, and the force at which the baseball is hit can be provided to various users, such as spectators of a baseball game or trainers seeking to optimize the player's swing. As discussed in more detail below, such information can be provided in conjunction with video of the player hitting the ball, either in near-real-time as the video is broadcast to spectators, as well as part of a replay of the hit. In this manner, embodiments of the present disclosure can provide information to enhance a spectator's experience (e.g., displaying the force applied to a baseball from a 420-foot home run) as well as to help players, trainers, and coaches improve an athlete's performance based on objective information.

Any number of different motion characteristics may be determined (110). The motion characteristics may include any desired information regarding the motion of an actor or equipment used by an actor, such as position, velocity, acceleration, orientation, rotation, translation, deformation, and changes to and combinations thereof. Embodiments of the present disclosure can determine multiple motion characteristics during a period of time that includes an athletic maneuver performed by an actor. For example, the orientation of a snowboarder executing a complex series of flips and rotations during jump can be monitored at various points or times (e.g., each millisecond) within a time period starting prior to the jump and ending after the jump.

Motion characteristics may include any number of different values, descriptions, and other information characterizing the movement (or lack thereof) of an actor and/or the actor's equipment. For example, a motion characteristic associated with an athletic maneuver may include one or more of: an orientation of the actor at a time within the time period (e.g., prior to, during, or after a maneuver), an angle of rotation around a local axis of the actor, a direction of rotation of the actor around a local axis of the actor, an angle of rotation around an absolute axis perpendicular to a plane of motion of the actor, a direction of rotation round an absolute axis perpendicular to a plane of motion of the actor, and combinations thereof.

Motion characteristics may be determined in any suitable manner. For example, motion characteristics can be determined based on two different frames of reference: an absolute frame of reference that is independent of the actor and independent of the actor's motion, and a local frame of reference that is associated with (i.e., connected to, and moves with) the actor. Embodiments of the present disclosure can characterize and measure a variety of different athletic maneuvers based on analyzing a combination of motion characteristics measured relative to the absolute and local frames of reference.

Embodiments of the present disclosure may also utilize information regarding the placement or location of various sensors in determining various motion characteristics. For example, determining rotation about an axis along the actor's body may be based on sensors (e.g., accelerometers and gyroscopes) being positioned collinearly with each other, such that sensors along one of the three-dimensional axes (x, y, and z) are the same. Embodiments of the present disclosure may also utilize calibration information from the sensors, such as calibration of various sensors performed during manufacturing of the sensors or installation of the sensors in an actor's clothing, equipment, etc. Various embodiments of the present disclosure may also be configured to communicate with various sensors (or control systems coupled thereto) to calibrate the sensors. Such calibration may be performed in any suitable manner. In one embodiment, for example, calibration of one or more inertial sensors attached to an actor and/or the actor's equipment can be calibrated when the sensors indicate the actor and/or equipment is not moving.

While multiple sensors may provide a large amount of information about an actor's motion, and the motion itself can be expressed by Euler angles, pitch, roll, and yaw, or rotational matrix, this information is understood by the participants, judges, and spectators only if it is expressed in the terms and sport-specific nomenclature that is commonly accepted in the sport performed by the actor. Embodiments of the present disclosure translate sensor observations to identify “tricks” using such nomenclature. Embodiments of the present disclosure can identify key motions for each trick, detect and measure all such motions, and then identify a trick and its quality or level of difficulty by comparing the detected motions with the nominal motions required for each trick.

While not exhaustive, the most common characteristics that define different tricks are: the orientation of the actor prior to the trick (forward facing, backward facing, left side facing, right side facing), the leading edge of the trick (e.g., starting from the front or end of the user's board), rotation around an absolute axis that is horizontal and perpendicular to the direction of motion during the trick (flip axis), and rotation around an axis associated with the body of the actor (self rotation axis). In characterizing a trick, a rotation angle around any axis does not have to be a full rotation but could be a “back and forth” swing at some particular angle, (e.g., a “Shifty” trick).

In various embodiments, determining the orientation of an actor (or the actor's equipment) at various points during a time period includes determining an angle of self-rotation for the actor and determining an angle of flip for the actor. For example, an actor (such as a skateboarder) performing a jump may be monitored using three gyroscopes and three accelerometers connected to the actor's equipment, body, or clothing. For this example, it is assumed that all the sensors are positioned in a device such that their local axes are collinear as described above. The orientation of the actor at time t with respect to a fixed reference frame is determined by a quaternion L(t). The quaternion change satisfies the following kinematic equation:

L_dot=½ω° L  (Equation 1)

In Equation 1, L_dot is a time derivative of L(t), ω is angular velocity of the body, measured by (for example) the gyroscopic sensors. As an initial condition, L(0) (i.e., the orientation of the actor before the jump) can be determined. Alternatively, the orientation of the actor immediately after the jump can be determined, namely L(t_max) where t_max is a time after the actor lands. The orientation of the actor (including the actor's facing/orientation prior to, or after, a maneuver) can be determined in any desired manner, such as based on a direction of a velocity vector calculated from the sensor data (e.g., from a GPS sensor) and the orientation of the actor calculated from other sensor data (e.g., from a magnetometer).

In a practical system, determining L(t_max) may be preferable where a landing shock (i.e., a large acceleration after the jump) in the opposite directed with respect to gravity is measurable. Given the orientation of the actor after the jump, the equation thus becomes:

L(t_max)≈[cos φ/2;e sin φ/2];

e=(g×a_shock)/(|g×a_shock|); and

φ=a cos((g·a_shock)/|g∥a_shock|).

Here, “≈” means that there may be some error associated with the final quaternion L(t_max), however this approximation may still be sufficient from practical point of view where the absolute orientation of the sportsman is not needed for calculation of turns during the jump.

Given the orientation at the end of the jump, L(t_max), the equations may be reformulated in classical Cauchy form (ODE with initial conditions), and using the new variable τ:

t=tmax−τ.

Substituting the given expression into Equation 1 obtains:

d/dt L=−d/dτL=½ω(tmax−τ)° L(tmax−τ).

Thus orientation of the sportsman can be determined solving the following Cauchy problem:

(d/dτL{tilde over ( )}(τ)=−½ω{tilde over ( )}(τ)° L{tilde over ( )}(τ)

L{tilde over ( )}(0)=L(tmax);

τε[0;tmax];

L{tilde over ( )}(τ)

L(tmax−τ); and

ω(τ)

ω{tilde over ( )}(tmax−τ))

In this example, a local vertical axis may be determined to (for example) calculate an angle of self-rotation of the actor during the jump. A local vertical axis may be determined in a variety of different ways, including based on an average of sensor data from one or more accelerometers prior to the athletic maneuver, an average of sensor data from one or more accelerometers after the athletic maneuver, sensor data from one or more accelerometers associated with a portion of the athletic maneuver (such as a landing performed after the maneuver), sensor data from one or more magnetometers (e.g., in conjunction with the nominal orientation of a magnetic vector relative to the vertical at the location of the event), and combinations thereof. FIG. 3 depicts an exemplary graph of sensor data measured from three accelerometers that shows the shock of an impact from an actor landing after a jump. In this graph, the horizontal graph axis depicts time in milliseconds and the vertical axis depicts acceleration in m/ŝ2. The three accelerometers correspond to acceleration along the x, y, and z sensor axes.

The angle of self-rotation for the actor may be determined in any suitable manner, including by calculating a path for each of a plurality of unit vectors in a local frame of reference that is associated with the actor, the plurality of unit vectors being orthogonal to a local vertical vector for the actor. The angle of self-rotation for the actor may then be selected as the largest rotation angle among such unit vectors.

Likewise, the angle of flip for the actor may be calculated in any desired manner, including by determining the motion of a vertical vector in a global frame of reference that is associated with the actor, identifying a plane of movement for the unit vertical vector, calculating a projection of the vertical vector on the plane, and selecting the angle of flip for the actor as the angle of the arc traveled by such projection on the plane.

FIG. 4 is an exemplary graph depicting self-rotation over time. In this example, sensor data from three gyroscopic sensors (corresponding to the x, y, and z planes as in FIG. 3) rotated around a nearly vertical axis in a nearly horizontal plane is plotted over time. In this graph, the vertical axis depicts rotation speed in rad/sec. Using the algorithms described above, the angle of self-rotation calculated for this data is about 363 degrees, and the angle of flip is calculated at less than 1 degree.

FIG. 5 is an exemplary graph depicting an angle of flip over time. In this example, sensor data from a gyroscopic sensor flipped (i.e., rotated about a nearly horizontal axis) is plotted over time (in milliseconds). FIG. 6A depicts the trajectory of the vector connected with the sensor that coincides with the vertical vector at the end of the rotation, while FIG. 6B depicts the projection of the trajectory on the plane of rotation (nearly round in this example). The resulting angle of flip rotation is about 358 degrees and the angle of self-rotation is about 38 degrees in this example.

Definitions for any desired athletic maneuvers can be received (115), associated with one or more motion characteristics (120), and stored in a database for future reference and retrieval (125). A definition for an athletic maneuver may apply to any movement or group of movements by an actor, the actor's equipment, or a combination thereof. For example, a back flip combined with a side flip performed by a snowboarder is often referred to as a “wildcat.” A definition for a wildcat maneuver may thus include the name (or aliases) of the maneuver, a textual description of the maneuver, and the motion characteristic(s) associated with the maneuver. Continuing this example, the motion characteristics associated with the wildcat may include indicators of the different axes of rotation by the actor's body and the rotation angles around these axes. Other information, such as a typical range of forces exerted on the actor's snowboard or parts of the actor's body and a range of time the snowboarder is airborne may be associated with the definition to help identify future wildcat jumps by comparing measured motion characteristics to the wildcat jump definition in the database.

The definition for an athletic maneuver may include any number of complex movements by an actor and/or the actor's equipment. For example, sensor data from sensors attached to the actor (such as a skateboarder) can be analyzed in conjunction with sensor data from the actor's equipment (such as a skateboarder). In this manner, rotations, flips, and other movement by the actor can be analyzed together with rotations and flips of the skateboard to identify all movements performed by the actor and provide a complete and accurate characterization of the maneuver performed. Embodiments of the present disclosure can thus be particularly effective in characterizing and identifying maneuvers that are complex and/or fast, therefore making it challenging for spectators and judges to identify all the movements involved.

A definition for an athletic maneuver may include, or be associated with, any other desired information. For example, statistics (including motion characteristics) for particular athletes who perform a maneuver may be linked to the definition of the maneuver in a database, allowing users of the systems and methods of the present disclosure to compare the manner in which various athletes perform the maneuver. Other information, such as video of the maneuver being performed, may likewise be included in, or linked to, the definition.

One or more determined motion characteristics can be compared to the motion characteristics associated with a known athletic maneuvers in a database to identify the maneuver (130) associated with the determined motion characteristics. The motion characteristics determined for an unknown maneuver may be compared to motion characteristics for known maneuvers in any suitable manner. For example, characteristics describing an actor's velocity, angle of self-rotation, angle of flip, and change in orientation during a time period can be compared to a relational database storing motion characteristics associated with the known maneuvers. Known motion characteristics in the database may be represented in nominal values and/or in ranges, reflecting that different actors may have different physical characteristics (e.g., height, weight), may have different equipment, may perform the same maneuver somewhat differently, and/or may perform a maneuver under various other conditions (e.g., with different types of ramps).

The database may also specify the quality of the maneuver that is associated with different parameter values. For example, a full rotation of 360 degrees during a jump may be valued in 10 points, while a partial rotation of only 350 dg (from jump start to landing) may be valued only at 9 points. The quality of a maneuver may be determined according to any desired factors, such as the difficulty of the maneuver.

A level of similarity between a determined set of motion characteristics and a known nominal set of motion characteristics may be determined (135). The level of similarity may be determined for each of a plurality of motion characteristics. Alternatively (or in addition), an overall level of similarity may be determined for an entire maneuver. The level(s) of similarity may be compared to various threshold values, and an alert generated if one or more similarity levels fail to meet a threshold value. In this manner, embodiments of the present disclosure can identify new (i.e., undefined) maneuvers and help administrators and users of the systems and methods described herein to identify errors in the values or associations of stored motion characteristics and modify them appropriately.

FIG. 1A depicts an exemplary method for populating a database with information regarding various athletic maneuvers. In this example, method 150 includes receiving sensor data (154) from a performed trick (152), and determining motion characteristics (156) and motion sequences (158) based on the received data. The motion characteristics and/or motion sequences may be averaged over other nominal cases (160) of the same trick, and a set of trick characteristics stored in the database (162).

Embodiments of the present disclosure may combine or overlay information identified and/or measured for an athletic maneuver (145) with any desired form of media. For example, information regarding an athletic maneuver can be combined or overlaid onto video taken of the athletic maneuver, thus providing near-real-time information on a maneuver to spectators or judges. Such an overlay can be synchronized in any desired manner, including by using a time measured from a global positioning system, a common communication network, or other sensor or common clock operating in conjunction with embodiments of the present disclosure.

Use of Gyro Sensors for Jump Detection

Jumps, and tricks performed during such jumps (e.g., while in the air), are often important aspects of many action sports. Accordingly, the detection and measurement of such jumps is likewise an important part of any system intended to quantify action sports. The typical approach for many conventional systems in detecting a jump or other athletic maneuver is to use the signal from an accelerometer. This approach assumes that during free fall the total nominal acceleration that can be measured by an acceleration sensor is zero. However, the practical application of this approach can be problematic.

For example, sensor errors, object (ski, snowboard, skateboard, motorcycle, etc.) vibration, and shocks mask the signal from an accelerometer during a jump or other athletic maneuver. FIG. 8 shows a graph of a signal from an accelerometer recorded from a snowboarder jump. Graph 800 shows the norm of acceleration during a snowboarder jump. The circles on the graph 800 mark the start and end of the jump. Similarly, FIG. 9 shows a graph of the norm of acceleration during a “kickflip” skateboard maneuver, with the circles on graph 900 showing the start and end of the kickflip.

In graph 800, the detection of the jump is somewhat straightforward, with the left-most circle (marking the start of the jump) and the right most having a much higher measured acceleration than the jump acceleration. Graph 900 in FIG. 9, however, shows that it is often very difficult to detect a an athletic maneuver based on acceleration when there is significant vibration. In particular, as shown in Graph 900, there is little difference between the acceleration measured at the left-most circle (marking the start of the kickflip) and the acceleration measured at the right-most circle (marking the end of the kickflip).

In addition to vibration, there is another complication of using low acceleration value as a jump indicator. During complicated athletic maneuvers, such as a “double cork,” where a skateboarder or snowboarder performs a double flip, sensors that are located away from the center of inertia can record significant rotational acceleration. FIG. 10 illustrates the norm of acceleration recorded during a double cork trick by an accelerometer installed on a snowboard. The plot shows that during the trick, while the athlete is still in the air and is nominally in “free fall,” acceleration can be as high as 4 g, which is way above a free fall nominal zero acceleration.

Accordingly, these examples show that it can be very difficult to detect and measure an athletic trick using acceleration alone. The inventors of the present application have discovered that measurements from a gyroscope (also referred to herein as a “gyro”) presents a number of advantages over the accelerometer signal. First, the measurements of a gyro are much less sensitive to vibration. Second, a gyro signal does not have a strong external signal as compared to gravity for an accelerometer. Third, in the absence of external forces and with no abrupt change in the object inertial characteristics, the gyro signal is relatively constant. Finally, many the landings performed in many athletic maneuvers is usually done on the front or back end of the sport equipment (snowboard, skateboard, motorcycle, etc) which leads to a sharp “shock” response on the gyro sensors.

FIG. 11 shows a plot 1100 of signals from three gyroscopes coupled to a skateboard during an “Ollie” maneuver, a maneuver where a skateboarder performs a jump with his/her skateboard. The plot clearly shows a typical lift of the board (rotation around short board axis) at the beginning of a jump and a typical landing “gyro shock.” FIG. 12 shows a graph 1200 of a derivative of a gyro signal during a snowboarding jump with a double cork. As can be seen in FIG. 12, the start of the double cork (between 0.5 and 1 seconds) and the end of the double cork (between 2.5 and 3 seconds) are better defined than based on an accelerometer signal alone as shown in FIG. 10.

The use of sensor data from one or more gyroscopes may be used in conjunction with method 100 depicted in FIG. 1 and described in more detail above. For example, the receipt of sensor data related to the motion of an actor and/or the actor's equipment (105) may include sensor data from a gyroscope, and determining the plurality of motion characteristics (110) may be based on a rate of change in the data from the gyroscope. The gyroscope sensor data can be used to help identify a variety of different motion characteristics and athletic maneuvers, such as the start of a jump and/or a landing. Data from gyro sensors may be used identify athletic maneuvers (130) alone or in conjunction with data from other types of sensors, such as an accelerometer.

In embodiments where data from both a gyroscope and an accelerometer are used together in determining motion characteristics, a fuzzy logic analysis may be performed on the data from the gyroscope and the data from the accelerometer.

After a jump is detected, tricks could be identified and measured by using object motion recreation via gyro integration. A common way to restore 3D motion is to integrate gyro using quaternions according to the following equation, where L is a quaternion associated with the body, L_dot is its time derivative, and Ω is a vector of angular velocity measured by gyro sensors:

L_dot=−½*ω*L

The initial conditions for the integration of this equation can be determined by averaging acceleration and magnetic vectors before or after the jump during some time period when gyro signal is small. Additionally, the orientation of an actor and/or the actor's equipment during the jump may be calculated to help can be used to filter out false alarm (false positive) for jump detection. In some embodiments, for example, the orientation of an actor and/or the actor's equipment may be determined based at least partially on a direction of a magnetic vector calculated using data from a magnetometer.

Embodiments of the disclosure may also be configured to automatically generate and transmit reports, statistics, and/or analyses based on information related to various athletic maneuvers. These may be provided in real-time or near-real-time to judges, spectators, social media outlets, broadcasting entities, websites, and other systems and entities. The computed jump and trick parameters and characteristics can be superimposed on a time-synchronized video to enhance the viewing experience and to provide more detailed information to spectators, coaches, and judges.

FIG. 2 is a block diagram of system which may be used in conjunction with various embodiments. While FIG. 2 illustrates various components of a computer system, it is not intended to represent any particular architecture or manner of interconnecting the components. Other systems that have fewer or more components may also be used.

In FIG. 2, the system 200 includes a computer system 210 comprising a processor 212, memory 214, and user interface 216. Computer system 210 may include any number of different processors, memory components, and user interface components, and may interact with any other desired systems and devices in conjunction with embodiments of the present disclosure. For example, the system 200 may include (or interact with) one or more databases (not shown) to allow the storage and retrieval of information such as the results of a jump/trick analysis, data from sensors attached to an actor or his/her equipment, trick definitions, and other data.

The functionality of the computer system 210, including the method depicted in FIGS. 1 and 13, (in whole or in part), may be implemented through the processor 212 executing computer-readable instructions stored in the memory 214 of the system 210. The memory 214 may store any computer-readable instructions and data, including software applications, applets, and embedded operating code.

The functionality of the system 210 or other system and devices operating in conjunction with embodiments of the present disclosure may also be implemented through various hardware components storing machine-readable instructions, such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) and/or complex programmable logic devices (CPLDs). Systems according to aspects of certain embodiments may operate in conjunction with any desired combination of software and/or hardware components. The processor 212 retrieves and executes instructions stored in the memory 214 to control the operation of the system 210. Any type of processor, such as an integrated circuit microprocessor, microcontroller, and/or digital signal processor (DSP), can be used in conjunction with embodiments of the present disclosure. A memory 214 operating in conjunction with embodiments of the disclosure may include any combination of different memory storage devices, such as hard drives, random access memory (RAM), read only memory (ROM), FLASH memory, or any other type of volatile and/or nonvolatile memory. Data (such as athletic maneuver definitions and associated motion characteristics) can be stored in the memory 214 in any desired manner, such as in a relational database.

The system 210 includes a user interface 216 may include any number of input devices (not shown) to receive commands, data, and other suitable input from a user such as input regarding the definitions of athletic maneuvers. The user interface 216 may also include any number of output devices (not shown) to provides the user with data, notifications, and other information. Typical I/O devices may include mice, keyboards, modems, network interfaces, printers, scanners, video cameras and other devices.

The system 210 may communicate with one or more sensor devices 220, as well as other systems and devices in any desired manner, including via network 230. The sensor devices 220 may include, or be coupled to, one or more control systems (not shown) through which the system 210 communicates with, or the system 210 may communicate directly with the sensors 220.

The system 210 may be, include, or operate in conjunction with, a server, a laptop computer, a desktop computer, a mobile subscriber communication device, a mobile phone, a personal digital assistant (PDA), a tablet computer, an electronic book or book reader, a digital camera, a video camera, a video game console, and/or any other suitable computing device.

The network 230 may include any electronic communications system or method. Communication among components operating in conjunction with embodiments of the present disclosure may be performed using any suitable communication method, such as, for example, a telephone network, an extranet, an intranet, the Internet, point of interaction device (point of sale device, personal digital assistant (e.g., iPhone®, Palm Pilot®, Blackberry®), cellular phone, kiosk, etc.), online communications, satellite communications, off-line communications, wireless communications, transponder communications, local area network (LAN), wide area network (WAN), virtual private network (VPN), networked or linked devices, keyboard, mouse and/or any suitable communication or data input modality. Systems and devices of the present disclosure may utilize TCP/IP communications protocols as well as IPX, Appletalk, IP-6, NetBIOS, OSI, any tunneling protocol (e.g. IPsec, SSH), or any number of existing or future protocols.

Creating and Enhancing Videos

Embodiments of the present disclosure may be used to collect, search, and compile new videos based on video collected from different sources, such as from multiple sources collecting video of an athletic event. In such cases, the different sources of video (e.g., individual spectators, a stationary camera, a point-of-view (POV) camera, and/or a drone) may be in different locations relative to a sporting event or athletic maneuvers being performed. Accordingly, one video source may have an exceptional vantage point (e.g., of a skier's run) compared to other sources for part of an athletic maneuver, while another video source may have a better view of the maneuver at a different time.

The quality and/or relevance of the video collected from each source may also vary. For example, different sources may be using different equipment, may be located different distances from an athletic event, may utilize different optical zoom values, or may benefit (or suffer) from various environmental conditions (e.g., sunlight, shadows, precipitation, etc.).

FIG. 13 illustrates an example of two video sources (cameras 1310 and 1320) providing video of an actor 1330 to the computing system 210 via network 230. Computing system 210 and network 230 are described above with reference to FIG. 2. In this example, cameras 1310 and 1320 are in different locations, and thus have different viewing perspectives of the actor 1430 as he/she performs various athletic maneuvers. Cameras 1310 and 1320 may provide pre-recorded video data to the computing device 230, or such video may be provided in real-time or near-real-time. The cameras 1310 and 1320 may also provide video directly to the computing system 210 (bypassing the network) or via one or more additional computing devices (such as a laptop, personal computer, smartphone, etc.). Cameras 1310 and 1320 may be (or include) a stationary camera, a camera embedded in a smartphone or other mobile device of a spectator, and/or a camera installed on a mobile drone or other mobile vehicle. Embodiments of the present disclosure may also receive video from a camera carried by the actor 1330 (e.g. installed on the actor's equipment or mounted on his/her body). Additionally, as described previously and in more detail below, sensor data (e.g., from sensors mounted on the actor and/or the actor's clothing or equipment) may be collected in conjunction with the video.

In this example, the actor 1330 performs some athletic maneuver (such as a ski jump or slalom) during action time period Ta: Ta1<t<Ta2. During this time interval Ta the actor 1330 moves along a trajectory Sa, represented in three dimensions as: [Xa(t), Ya(t), Za(t)] and with orientation Wa represented by pitch, roll, and yaw as: [Pa(t),Ra(t),Ya(t)]. During time interval Ta this actor 1330 performs one or more athletic maneuvers, such as jumps, tricks, cutbacks, high acceleration, etc. represented as events: E=[Ea1, Ea2, . . . Eam]. As described herein, sensors coupled to the actor (including the actor's equipment) may be used to determine various motion characteristics and such characteristics used to identify an athletic maneuver.

In the example shown in FIG. 13, video of the actor 1330 is captured by one or more video sources at time periods Tk: Tk1<t<Tk2; k=1, . . . n, such that intersection of each time intervals Tk with time interval Ta is not empty. The video may be captured by professional camera operators (e.g., hired to cover the event), spectators using smartphones or other handheld cameras, automated or semi automated devices (such as mounted cameras and/or drones) as well as from other sources. Such video capture may be performed asynchronously (i.e., one or more video sources are not capturing video at the same time). Additionally, such video may be captured without coordination between the sources (e.g., two spectators at opposite ends of a stadium capture video of the same athletic trick).

Any of the various video sources (e.g., cameras 1310 and 1320 in FIG. 13) may be stationary or moving during the video capture of the actor 1330. For example, the camera 1310 may be stationary while camera 1320 may be moving along a trajectory Sk in three-dimensional space represented as: (Xk(t),Yk(t),Zk(t)], and has orientation and zoom values WZk: [Pk(t),Rk(t),Yk(t),ZMk(t)] (Pitch, Roll, Yaw, and Zoom).

The video captured from cameras 1310 and 1320 (and other video sources), as well as information on the actor's motion (e.g., Sa, Wa, and Ea) can be provided to computing device 210 and used to generate a combined video that automatically selects optimal video from among the different sources, thus quickly, efficiently, and cost-effectively providing a superior visual experience of an event. Trajectory and sensory information (e.g., recorded by sensors attached to the actor or the actor's equipment) may also be provided to the computing device 210.

FIG. 14 depicts an exemplary process that may be performed in accordance with various systems of the present disclosure, including the system depicted in FIG. 13. In FIG. 14, exemplary method 1400 includes receiving video of an actor from multiple sources (1405), receiving sensor data pertaining to one or more of the video sources (1410), determining video source characteristics (1415), and synchronizing video from multiple sources (1420). The method 1400 further includes receiving sensor data pertaining to the actor being videoed (1425), determining motion characteristics for the actor based on the actor's sensor data (1430), and identifying an athletic maneuver performed by the actor (1435). Video from one or more sources is combined (1440), information is overlayed on the combined video (1445), and the combined video is presented to one or more users (1450). The combined video may also be tagged and stored for later retrieval (1455).

Video may be received (1405) from any number and type of source. For example, video may be captured by a combination of professionally-operated cameras and cameras operated by spectators at an event. Such video may be captured and delivered to a system implementing the functionality of method 1400 (such as computing device 210) in real-time or near-real time. Such video may also be stored (e.g., in a database or other data store) and retrieved by the computing device 210 for processing.

Video from different sources may be captured at different times, and such times may overlap. For example, a first video source might capture the first minute of an actor's two-minute downhill ski run, while a second video source might capture the ski run from 30 seconds into the run until the end of the run.

Data from sensors associated with a video source may be received (1410) and used to determine characteristics of the video source (1415). For example, a video source may be equipped with a global positioning system (GPS) that provides information on the position of a video source (such as a digital video camera), and inertial and/or magnetic sensors that provide information on the orientation of the video source as it captures video. The video source may also provide information such as its zoom factor and a time indicator, as well as information on a user or author of the video, information on the actor, information on the sporting event the actor is participating in, and the like. As with the video itself, sensor data and other information related to a video source may be provided in real-time or near-real-time, and can be stored and retrieved from a database or other data store.

Information regarding a video source may be used, for example, to determine: a trajectory of a moving video source, an orientation of a video source, a zoom factor of the video source (e.g., 2×, 3×, 4×, etc.), and/or a viewing direction between the video source and the actor being videoed. As described below with respect to step 1440, these characteristics can be used to select the best frames of video for the combined video.

Video from multiple sources is time-synchronized (1420). In some embodiments, the video from a source may include a set of time tags which can be used to synchronize video from two or more sources based on a time frame common to the sources. The common time frame may be based, for example, on time information from a network, a global positioning satellite, or other system in communication with the two or more video sources. In an example where the time tags are marked at each second, video from a first source that records the entire 30 seconds of a 30-second event (i.e., T1, T2, . . . T30) can be compared to video from a second source that includes video of an event starting from ten seconds into the event until the end (i.e., T10, T11, . . . T30). As described in more detail below, the video from T1 to T9 in the first video may be used as the best (i.e., only) video in a combined video showing the event, while the video frames from each of the two sources between T10 through T30 can be compared to identify the best video to include in the combined video.

Method 1400 further includes receiving sensor data pertaining to an actor (1425), determining one or more motion characteristics based on the actor's sensor data (1430), and identifying an athletic maneuver based on the one more motion characteristics (1435). These steps are described in more detail above with regards to steps 105-130 in FIG. 1. Any of the sensors and motion characteristics may likewise be employed in conjunction with the method 1400 shown in FIG. 14. Among other things, embodiments of the present disclosure can automatically identify an athletic maneuver based on sensor data from the actor (e.g., sensors mounted on the actor, the actor's clothing, and/or the actor's equipment) and identify the time period during which athletic maneuver takes place in order to automatically identify the frames of video from different sources that may have captured the athletic maneuver, and select the best video of the maneuver from all the possible sources. In various embodiments, sensor data pertaining to an actor may include time tags, just as the video of the actor includes time tags. In such embodiments, embodiments of the disclosure may synchronize the time tags on sets of sensor data from two or more different sensors based on a common time frame, such as time information from a network or global positioning satellite. The sensor data from different sensors measuring an athletic event may thus be correlated with video of the athletic event by synchronizing the time tags on the data from each of the sensors and each of the video sources.

For example, referring again to FIG. 13, assume video sources 1310 and 1320 both capture video of actor 1330 performing a sixty-second ski run between time T1-T60. Based on a time frame common to the two video sources (1310 and 1320) and the sensors associated with the actor 1330 (e.g., using time information from a global positioning satellite) if the system determines, based on the actor's sensor data, that the actor 1330 performs a jump between T28 and T30, the video frame sequences from video sources 1310 and 1320 between T28 and T30 can be analyzed to identify the best video sequence to include in the combined video. In some embodiments, video of an athletic maneuver may be automatically modified (e.g., sped up or slowed down) in response to identifying a particular athletic maneuver. For example, when a particular snowboarding trick (such as a jump or a flip) is identified, the video of that trick may be slowed down to provide the viewer with a better viewing experience of an otherwise fast-moving event. Other video modifications, such as overlaying computer-generated graphics or other information on the video may be performed. Such graphics may include, for example, graphics showing a snow board moving and rotating, as well as the motion of an avatar representing the actor, in order to help viewers understand an athletic maneuver performed by the actor.

Method 1400 further includes creating combined video (1440) using video from one or more video sources. Even in cases where a single video source is in the best position to view an athletic event and produces the highest quality video, embodiments of the present disclosure can be used to quickly analyze video from other possible sources to verify the single source is the best choice to provide the video.

In many other cases, however, the best video of an athletic event will be provided from multiple sources. Characteristics of each video source (determined at 1415) can be used to identify the best video sequences for a combined video. For example, embodiments of the present disclosure may analyze characteristics of a video source, such as the source's trajectory (if moving), orientation, zoom factor, distance to the actor being videoed, viewing direction between the video source and the actor, the quality of the video source's equipment, characteristics of the video frames captured by each source (e.g., brightness, color, frame rate, signal-to-noise ratio, and/or any other video quality metric), a type of athletic maneuver being performed by the actor, and/or other characteristics to assign a frame quality score to one or more frames of video from the source. The frame quality score can then be compared to the frame quality score of video from another source over the same time period to determine which frame(s) should be included in the combined video. The frame quality score can be based on a single factor or on multiple factors, including any combination of video source characteristics. The frame quality score may be assigned by a computer (e.g., according to an algorithm or script) as well as by a human operator.

In many cases, it is preferable to provide a sequence of frames from a single video source for inclusion in the combined video even if each frame in the sequence is not necessarily the highest quality frame available, since rapidly switching views from different cameras can actually degrade the overall viewing experience of a user. In such cases, a frame quality score can be assigned to a group of frames based on, for example, the average score for each individual frame. Additionally, a sequence of frames from a first source may be included in the combined video even if they are of lower quality than a sequence of frames from another source based on frames from the first source being previously or subsequently selected for the combined video (thus favoring a seamless display of video over rapid cuts between sources). In this manner, embodiments of the present disclosure can use the characteristics determined for each video source to identify the video frames to include in the combined video that will provide the best overall experience for a user.

In some embodiments, frames from different sources may be displayed simultaneously (e.g., via a split-screen) in the combined video. For example, where one video source captures a ski jump from a one angle, while another video source (such as a drone hovering overhead) captures the jump from another angle, the combined video may include one or more frames from the first video source and one or more frames from the second video source, captured at the same synchronized time and displayed simultaneously.

A variety of information may be overlaid (1445) on the combined video, including information from the video source characteristics, the determined motion characteristics, sensor data from the actor and/or the video source, and other information. Information may be overlaid and synchronized with the video based on time measured from a common time reference as described above. As described in more detail below with reference to FIG. 15, videos may be captured and stored in a database or other data store for later retrieval, processing, and combination.

The combined video may be presented (1450) to one or more users via a display device or in any other suitable manner. In one exemplary embodiment, the combined video may be presented in a split-screen format, whereby synchronized video from two or more sources is simultaneously displayed on the same screen. In such embodiments, the system need not select only a single set of frames for inclusion in the combined video, rather multiple sets of frames from multiple sources can be included in the combined video.

Additionally, multiple frame sets need not be included for the entire combined video. For example, the combined video may comprise a single view of the actor for a portion of the video, and display multiple views (e.g., the best 2, 3, 4, etc. frame sequences) of a particularly significant athletic event (e.g., a difficult trick performed by the actor) identified by the actor sensor data.

The combined video (as well as any received videos) may be tagged and/or stored (1455) for later retrieval and use. Video may be tagged with any number of tags containing any desired information related to the athletic maneuver, such as the name of the athlete, the name of the athletic maneuver, a rating associated with the performance of the athletic maneuver (e.g., given by judges of the maneuver), as well as various statistics of the athletic maneuver (e.g., the height of a jump, the rotation angle of a flip, and/or the maximum speed attained by an actor during a maneuver). A tagged video (with or without overlay) can be stored in a database or other data store so that it can be retrieved by searching on any of the tags, such as “find all surfing video at Huntington Beach Pier with air reverse higher than 3 feet.”

Referring now to FIG. 15, method 1500 displays an exemplary process for retrieving videos and creating one or more combined videos. In this example, a system receives a request to view a particular activity (1505), such as one or more athletic maneuvers performed by an actor. The activity viewing request may include any desired information describing the activity, such as a date, location, time, type of athletic maneuver, name of the actor performing the activity, location where the activity takes place, and other information. The system may prompt the user for additional information, such as to select from among a list of possible activities that match the user's search criteria. Based on the activity viewing request, the time and position (or ranges of each) is determined (1510). One or more videos capturing the activity are selected (1515) and quality scores assigned (1520) thereto. A combined video depicting the requested activity may be created (1525) based on the selected videos and their respective quality scores. The determination and assignment of quality scores, as well as the creation of combined video, may be performed in the same manner described above with reference to FIG. 14.

While some embodiments can be implemented in fully functioning computers and computer systems, various embodiments are capable of being distributed as a computing product in a variety of forms and are capable of being applied regardless of the particular type of machine or computer-readable media used to actually effect the distribution.

A machine readable medium can be used to store software and data which when executed by a data processing system causes the system to perform various methods. The executable software and data may be stored in various places including for example ROM, volatile RAM, non-volatile memory and/or cache. Portions of this software and/or data may be stored in any one of these storage devices. Further, the data and instructions can be obtained from centralized servers or peer to peer networks. Different portions of the data and instructions can be obtained from different centralized servers and/or peer to peer networks at different times and in different communication sessions or in a same communication session. The data and instructions can be obtained in entirety prior to the execution of the applications. Alternatively, portions of the data and instructions can be obtained dynamically, just in time, when needed for execution. Thus, it is not required that the data and instructions be on a machine readable medium in entirety at a particular instance of time.

Examples of computer-readable media include but are not limited to recordable and non-recordable type media such as volatile and non-volatile memory devices, read only memory (ROM), random access memory (RAM), flash memory devices, floppy and other removable disks, magnetic disk storage media, optical storage media (e.g., Compact Disk Read-Only Memory (CD ROMS), Digital Versatile Disks (DVDs), etc.), among others. The computer-readable media may store the instructions.

In various embodiments, hardwired circuitry may be used in combination with software instructions to implement the techniques. Thus, the techniques are neither limited to any specific combination of hardware circuitry and software nor to any particular source for the instructions executed by the data processing system.

Although some of the drawings illustrate a number of operations in a particular order, operations which are not order dependent may be reordered and other operations may be combined or broken out. While some reordering or other groupings are specifically mentioned, others will be apparent to those of ordinary skill in the art and so do not present an exhaustive list of alternatives. Moreover, it should be recognized that the stages could be implemented in hardware, firmware, software or any combination thereof.

For the sake of brevity, conventional data networking, application development and other functional aspects of the systems (and components of the individual operating components of the systems) may not be described in detail herein. Furthermore, the connecting lines shown in the various figures contained herein are intended to represent exemplary functional relationships and/or physical couplings between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may be present in a practical system.

The various system components discussed herein may include one or more of the following: a host server or other computing systems including a processor for processing digital data; a memory coupled to the processor for storing digital data; an input digitizer coupled to the processor for inputting digital data; an application program stored in the memory and accessible by the processor for directing processing of digital data by the processor; a display device coupled to the processor and memory for displaying information derived from digital data processed by the processor; and a plurality of databases. Various databases used herein may include: shipping data, package data, and/or any data useful in the operation of the system.

Various functionality may be performed via a web browser and/or application interfacing utilizing a web browser. Such browser applications may comprise Internet browsing software installed within a computing unit or a system to perform various functions. These computing units or systems may take the form of a computer or set of computers, and any type of computing device or systems may be used, including laptops, notebooks, tablets, hand held computers, personal digital assistants, set-top boxes, workstations, computer-servers, main frame computers, mini-computers, PC servers, network sets of computers, personal computers and tablet computers, such as iPads, iMACs, and MacBooks, kiosks, terminals, point of sale (POS) devices and/or terminals, televisions, or any other device capable of receiving data over a network. Various embodiments may utilize Microsoft Internet Explorer, Mozilla Firefox, Google Chrome, Apple Safari, Opera, or any other of the myriad software packages available for browsing the internet.

Various embodiments may operate in conjunction with any suitable operating system (e.g., Windows NT, 95/98/2000/CE/Mobile/, Windows 7/8, OS2, UNIX, Linux, Solaris, MacOS, PalmOS, etc.) as well as various conventional support software and drivers typically associated with computers. Various embodiments may include any suitable personal computer, network computer, workstation, personal digital assistant, cellular phone, smart phone, minicomputer, mainframe or the like. Embodiments may implement security protocols, such as Secure Sockets Layer (SSL), Transport Layer Security (TLS), and Secure Shell (SSH). Embodiments may implement any desired application layer protocol, including http, https, ftp, and sftp.

The various system components may be independently, separately or collectively suitably coupled to a network via data links which includes, for example, a connection to an Internet Service Provider (ISP) over the local loop as is typically used in connection with standard modem communication, cable modem, satellite networks, ISDN, Digital Subscriber Line (DSL), or various wireless communication methods. It is noted that embodiments of the present disclosure may operate in conjunction with any suitable type of network, such as an interactive television (ITV) network.

The system may be partially or fully implemented using cloud computing. “Cloud” or “Cloud computing” includes a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing may include location-independent computing, whereby shared servers provide resources, software, and data to computers and other devices on demand.

Various embodiments may be used in conjunction with web services, utility computing, pervasive and individualized computing, security and identity solutions, autonomic computing, cloud computing, commodity computing, mobility and wireless solutions, open source, biometrics, grid computing and/or mesh computing.

Any databases discussed herein may include relational, hierarchical, graphical, or object-oriented structure and/or any other database configurations. Moreover, the databases may be organized in any suitable manner, for example, as data tables or lookup tables. Each record may be a single file, a series of files, a linked series of data fields or any other data structure. Association of certain data may be accomplished through any desired data association technique such as those known or practiced in the art. For example, the association may be accomplished either manually or automatically.

Any databases, systems, devices, servers or other components of the system may be located at a single location or at multiple locations, wherein each database or system includes any of various suitable security features, such as firewalls, access codes, encryption, decryption, compression, decompression, and/or the like.

Encryption may be performed by way of any of the techniques now available in the art or which may become available—e.g., Twofish, Blowfish, RSA, El Gamal, Schorr signature, DSA, PGP, PKI, and symmetric and asymmetric cryptosystems.

Embodiments may connect to the Internet or an intranet using standard dial-up, cable, DSL or any other Internet protocol known in the art. Transactions may pass through a firewall in order to prevent unauthorized access from users of other networks.

The computers discussed herein may provide a suitable website or other Internet-based graphical user interface which is accessible by users. For example, the Microsoft Internet Information Server (IIS), Microsoft Transaction Server (MTS), and Microsoft SQL Server, may be used in conjunction with the Microsoft operating system, Microsoft NT web server software, a Microsoft SQL Server database system, and a Microsoft Commerce Server. Additionally, components such as Access or Microsoft SQL Server, Oracle, Sybase, Informix MySQL, Interbase, etc., may be used to provide an Active Data Object (ADO) compliant database management system. In another example, an Apache web server can be used in conjunction with a Linux operating system, a MySQL database, and the Perl, PHP, and/or Python programming languages.

Any of the communications, inputs, storage, databases or displays discussed herein may be facilitated through a website having web pages. The term “web page” as it is used herein is not meant to limit the type of documents and applications that might be used to interact with the user. For example, a typical website might include, in addition to standard HTML documents, various forms, Java applets, JavaScript, active server pages (ASP), common gateway interface scripts (CGI), extensible markup language (XML), dynamic HTML, cascading style sheets (CSS), AJAX (Asynchronous Javascript And XML), helper applications, plug-ins, and the like. A server may include a web service that receives a request from a web server, the request including a URL and an IP address. The web server retrieves the appropriate web pages and sends the data or applications for the web pages to the IP address. Web services are applications that are capable of interacting with other applications over a communications means, such as the Internet.

Various embodiments may employ any desired number of methods for displaying data within a browser-based document. For example, data may be represented as standard text or within a fixed list, scrollable list, drop-down list, editable text field, fixed text field, pop-up window, and the like. Likewise, embodiments may utilize any desired number of methods for modifying data in a web page such as, for example, free text entry using a keyboard, selection of menu items, check boxes, option boxes, and the like.

The exemplary systems and methods illustrated herein may be described in terms of functional block components, screen shots, optional selections and various processing steps. It should be appreciated that such functional blocks may be realized by any number of hardware and/or software components configured to perform the specified functions. For example, the system may employ various integrated circuit components, e.g., memory elements, processing elements, logic elements, look-up tables, and the like, which may carry out a variety of functions under the control of one or more microprocessors or other control devices. Similarly, the software elements of the system may be implemented with any programming or scripting language such as C, C++, C#, Java, JavaScript, VBScript, Macromedia Cold Fusion, COBOL, Microsoft Active Server Pages, assembly, PERL, PHP, AWK, Python, Visual Basic, SQL Stored Procedures, PL/SQL, any UNIX shell script, and extensible markup language (XML) with the various algorithms being implemented with any combination of data structures, objects, processes, routines or other programming elements. Further, it should be noted that the system may employ any number of conventional techniques for data transmission, signaling, data processing, network control, and the like. Still further, the system could be used to detect or prevent security issues with a client-side scripting language, such as JavaScript, VBScript or the like.

The systems and methods of the present disclosure may be embodied as a customization of an existing system, an add-on product, a processing apparatus executing upgraded software, a stand alone system, a distributed system, a method, a data processing system, a device for data processing, and/or a computer program product. Accordingly, any portion of the system or a module may take the form of a processing apparatus executing code, an internet based embodiment, an entirely hardware embodiment, or an embodiment combining aspects of the internet, software and hardware. Furthermore, the system may take the form of a computer program product on a computer-readable storage medium having computer-readable program code means embodied in the storage medium. Any suitable computer-readable storage medium may be utilized, including hard disks, CD-ROM, optical storage devices, magnetic storage devices, and/or the like.

The system and method is described herein with reference to screen shots, block diagrams and flowchart illustrations of methods, apparatus (e.g., systems), and computer program products according to various embodiments. It will be understood that each functional block of the block diagrams and the flowchart illustrations, and combinations of functional blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by computer program instructions.

These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions that execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart block or blocks. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

Accordingly, functional blocks of the block diagrams and flowchart illustrations support combinations of means for performing the specified functions, combinations of steps for performing the specified functions, and program instruction means for performing the specified functions. It will also be understood that each functional block of the block diagrams and flowchart illustrations, and combinations of functional blocks in the block diagrams and flowchart illustrations, can be implemented by either special purpose hardware-based computer systems which perform the specified functions or steps, or suitable combinations of special purpose hardware and computer instructions. Further, illustrations of the process flows and the descriptions thereof may make reference to user windows, webpages, websites, web forms, prompts, etc. Practitioners will appreciate that the illustrated steps described herein may comprise in any number of configurations including the use of windows, webpages, web forms, popup windows, prompts and the like. It should be further appreciated that the multiple steps as illustrated and described may be combined into single webpages and/or windows but have been expanded for the sake of simplicity. In other cases, steps illustrated and described as single process steps may be separated into multiple webpages and/or windows but have been combined for simplicity.

The term “non-transitory” is to be understood to remove only propagating transitory signals per se from the claim scope and does not relinquish rights to all standard computer-readable media that are not only propagating transitory signals per se. Stated another way, the meaning of the term “non-transitory computer-readable medium” should be construed to exclude only those types of transitory computer-readable media which were found in In Re Nuijten to fall outside the scope of patentable subject matter under 35 U.S.C. §101.

Benefits, other advantages, and solutions to problems have been described herein with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any elements that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as critical, required, or essential features or elements of the disclosure.

Although the disclosure includes a method, it is contemplated that it may be embodied as computer program instructions on a tangible computer-readable carrier, such as a magnetic or optical memory or a magnetic or optical disk. All structural, chemical, and functional equivalents to the elements of the above-described exemplary embodiments that are known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the present claims. Moreover, it is not necessary for a device or method to address each and every problem sought to be solved by the present disclosure, for it to be encompassed by the present claims. Furthermore, no element, component, or method step in the present disclosure is intended to be dedicated to the public regardless of whether the element, component, or method step is explicitly recited in the claims. No claim element herein is to be construed under the provisions of 35 U.S.C. 112, sixth paragraph, unless the element is expressly recited using the phrase “means for.” As used herein, the terms “comprises”, “comprising”, or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

Where a phrase similar to “at least one of A, B, or C,” “at least one of A, B, and C,” “one or more A, B, or C,” or “one or more of A, B, and C” is used, it is intended that the phrase be interpreted to mean that A alone may be present in an embodiment, B alone may be present in an embodiment, C alone may be present in an embodiment, or that any combination of the elements A, B and C may be present in a single embodiment; for example, A and B, A and C, B and C, or A and B and C.

Changes and modifications may be made to the disclosed embodiments without departing from the scope of the present invention. These and other changes or modifications are intended to be included within the scope of the present disclosure, as expressed in the following claims. 

What is claimed is:
 1. A computer-implemented method comprising: receiving, by a computer system, first video of an actor from a first source, wherein the first video is captured during a first time period; receiving, by the computer system, second video of the actor from a second source, wherein the second video is captured during a second time period, and wherein the first time period and second time period at least partially overlap; receiving, by a computer system, sensor data related to motion by the actor over one or more of the first time period and the second time period; determining, by the computer system and based on the sensor data, a plurality of motion characteristics; identifying, based on the plurality of motion characteristics, an athletic maneuver associated with the motion; creating, by the computer system, a combined video that displays the actor performing the athletic maneuver and includes at least one frame of the first video from the first source and at least one frame of the second video from the second source.
 2. The method of claim 1, wherein the first video includes a first set of time tags and the second video includes a second set of time tags and the method further includes synchronizing the first set of time tags and the second set of time tags based on a common time frame.
 3. The method of claim 2, wherein the common time frame is based on time information received from a network in communication with the first source and the second source.
 4. The method of claim 2, wherein the common time frame is based on time information received from a global positioning satellite in communication with the first source and the second source.
 5. The method of claim 1, wherein the combined video includes a first frame from the first video source and a second frame from the second video source, wherein the first frame and the second frame are taken at the same synchronized time, and wherein combined video displays the first frame and the second frame simultaneously.
 6. The method of claim 1, wherein receiving the sensor data related to the motion by the actor includes: receiving a first set of sensor data from a first sensor that includes a first set of time tags; receiving a second set of sensor data from a second sensor that includes a second set of time tags; and synchronizing the first set of time tags and the second set of time tags based on a common time frame based on time information from one or more of a network in communication with the first sensor and the second sensor, and a global positioning satellite in communication with the first sensor and the second sensor.
 7. The method of claim 1, further comprising: receiving sensor data related to the first source during the first time period; and determining, based on the sensor data related to the first source, one or more of: a trajectory of the first source during the first time period, an orientation of the first source during the first time period, and a zoom factor for the first source during the first time period.
 8. The method of claim 7, wherein determining the plurality of motion characteristics includes determining a position of the actor during the first time period, and wherein the method further comprises: determining, based on the trajectory and the orientation of the first source during the first time period, a viewing direction between the first source and the actor during the first time period.
 9. The method of claim 7, further comprising: receiving sensor data related to the second source during the second time period; and determining, based on the sensor data related to the second source, one or more of: a trajectory of the second source during the second time period, an orientation of the second source during the second time period, and a zoom factor for the second source during the first time period.
 10. The method of claim 9, wherein determining the plurality of motion characteristics includes determining a position of the actor during the second time period, and wherein the method further comprises: determining, based on the trajectory and the orientation of the second source during the second time period, a viewing direction between the second source and the actor during the second time period.
 11. The method of claim 10, wherein creating the combined video includes selecting a frame of video from the first video or a frame of video from the second video based on the viewing direction between the first source the actor, and the viewing direction between the second source and the actor.
 12. The method of claim 11, wherein selecting the frame of video includes assigning a first frame quality score to the frame of video from the first video based on the viewing direction between the first source and the actor, and assigning a second frame quality score to the frame of video from the second video on the viewing direction between the second source the actor.
 13. The method of claim 1, wherein creating the combined video includes: assigning a first frame quality score to a frame of video from the first video based on one or more of: a distance between the first source and the actor, a viewing direction between the first source and the actor, a type of athletic maneuver being performed by the actor, and a zoom factor for the first source; assigning a second frame quality score to a frame of video from the second video based on one or more of: a distance between the second source and the actor, a viewing direction between the second source and the actor, a type of athletic maneuver being performed by the actor, and a zoom factor for the second source; and selecting the frame from the first video or the frame from the second video based on the first frame quality score and the second frame quality score.
 14. The method of claim 13, wherein selecting the frame is further based on whether the first source or the second source is selected to provide one or more previously-selected frames.
 15. The method of claim 1, wherein the plurality of motion characteristics are selected from the group consisting of: position; velocity; acceleration; orientation; rotation; translation; deformation; and combinations thereof.
 16. The method of claim 1, wherein the sensor data includes data from a sensor selected from the group consisting of: a gyroscope; an accelerometer; a magnetometer; a Hall effect sensor; a global positioning system; an ultrasonic sensor; an optical sensor; a barometric sensor; and combinations thereof.
 17. The method of claim 1, wherein identifying the athletic maneuver includes comparing the plurality of determined motion characteristics to one or more motion characteristics associated with a known athletic maneuver.
 18. The method of claim 17, wherein identifying the athletic maneuver further includes determining a level of similarity between the plurality of determined motion characteristics and the motion characteristics associated with the known athletic maneuver.
 19. The method of claim 17, wherein the known athletic maneuver is stored in a database with one or more associated motion characteristics.
 20. The method of claim 1, further comprising: overlaying information related to the athletic maneuver on the combined video of the actor performing the athletic maneuver, wherein the information related to the athletic maneuver is based on the plurality of motion characteristics.
 21. The method of claim 20, wherein overlaying the information related to the athletic maneuver includes synchronizing the overlaid information and the video based on a time measured from a common time reference.
 22. The method of claim 1, further comprising presenting the combined video to a user via a display screen in communication with the computer system.
 23. The method of claim 1, further comprising: tagging the combined video with information related to the athletic maneuver; and storing the combined video in a data store, wherein the combined video is searchable via the tag.
 24. The method of claim 1, wherein creating the combined video includes modifying, in response to identifying the athletic maneuver, at least a portion of the video displaying the actor performing the athletic maneuver.
 25. The method of claim 24, wherein modifying the video includes one or more of: speeding up the video, and slowing down the video.
 26. A non-transitory, computer-readable medium storing instructions that, when executed, cause a computer system to: receive first video of an actor from a first source, wherein the first video is captured during a first time period; receive second video of the actor from a second source, wherein the second video is captured during a second time period, and wherein the first time period and second time period at least partially overlap; receive sensor data related to motion by the actor over one or more of the first time period and the second time period; determine, based on the sensor data, a plurality of motion characteristics; identify, based on the plurality of motion characteristics, an athletic maneuver associated with the motion; create a combined video that displays the actor performing the athletic maneuver and includes at least one frame of the first video from the first source and at least one frame of the second video from the second source.
 27. A computer system comprising: a processor; and memory in communication with the processor and storing instructions that, when executed by the processor, cause the computer system to: receive first video of an actor from a first source, wherein the first video is captured during a first time period; receive second video of the actor from a second source, wherein the second video is captured during a second time period, and wherein the first time period and second time period at least partially overlap; receive sensor data related to motion by the actor over one or more of the first time period and the second time period; determine, based on the sensor data, a plurality of motion characteristics; identify, based on the plurality of motion characteristics, an athletic maneuver associated with the motion; create a combined video that displays the actor performing the athletic maneuver and includes at least one frame of the first video from the first source and at least one frame of the second video from the second source.
 28. A computer system comprising: a processor; and memory in communication with the processor and storing instructions that, when executed by the processor, cause the computer system to: retrieve first video of an actor captured by a first source from a database in communication with the computer system, wherein the first video is captured during a first time period; retrieve second video of the actor captured by a second source from the database, wherein the second video is captured during a second time period, and wherein the first time period and second time period at least partially overlap; retrieve, from the database, sensor data related to motion by the actor over one or more of the first time period and the second time period; determine, based on the sensor data, a plurality of motion characteristics; identify, based on the plurality of motion characteristics, an athletic maneuver associated with the motion; create a combined video that displays the actor performing the athletic maneuver and includes at least one frame of the first video from the first source and at least one frame of the second video from the second source. 